Circadian periodicity of heart rate variability in hospitalized angor patients

Abstract

The relationship between unstable angor (angina) and circadian periodicity of heart rate variability (HRV) was explored in a group of patients hospitalized in a coronary care unit (CCU). Patients were classified as normal (whose symptoms had non-cardiovascular origin, n=8), moderate angor (n=13) and severe angor (n=11). A fourth group of ambulatory healthy volunteers (n=12) was included. Individual 24 h Holter records were analyzed, mean RR and standard deviation of RR (SDNN) being obtained from 1 h-length windows. For frequency domain analysis, 5 min-length windows were employed. The spectral components analyzed were total power (spectral power between 0.01 and 0.5 Hz), low frequency power (LF: power between 0.04 and 0.15 Hz), and high frequency power (HF: power between 0.15 and 0.4 Hz). In addition, LF to HF areas ratio (L/H) was computed. Mesor, amplitude and acrophase for every 24 h rhythm were calculated by cosinor analysis. As compared to ambulatory controls, admission to the CCU diminished amplitude and phase-delayed the circadian oscillation of most HRV parameters, except for SDNN. Moderate angor patients showed decreased amplitude of RR and L/H and augmented amplitude of SDNN when compared to normal hospitalized subjects. A phase delay of about 1.5 h for RR intervals and a phase advance of 3.5-6 h for LFA and SDNN were found in the moderate angor group when compared to normal. Amplitude of 24 h variation of total power decreased in severely angor patients and the circadian oscillation of HF (an indicator of vagal control on the heart) became free running. A phase delay of 2.5 h in SDNN acrophase was found in severely affected patients when compared to moderate. The results indicate that severity of unstable angor correlates with desynchronization of parasympathetic control of heart rate.

Sleep Habits, Alertness, Cortisol Levels, and Cardiac Autonomic Activity in Short-Distance Bus Drivers

“…Although cardiovascular measures such as heart rate and heart rate variability may serve as early indicators of fatigue 36 our results seems to follow the normal circadian pattern of HRV measures, with a nadir for HF-HRV in the afternoon and an increase of LF relative predominance toward noon. 37,38 Finally, we found a high prevalence of overweight and obesity, especially in the morning shift. Short sleep duration is associated with a modest increase in future weight gain and incident obesity, as informed in a study that observed 68,000 women during 16 years.…”

The circadian pattern of cardiac autonomic modulation and obesity in adolescents

“…After excluding individuals who did not have sufficient ECG data, 388 individuals contributed up to 48 segments of 30-minute RR interval data, thus we analyzed 18624 segments total A two-stage analysis was performed to assess the relationship of obesity and population attributes on the circadian pattern of HRV. At stage-1, for each individual we fit the HRV data based on all 48 segments to a cosine periodic regression model [21] using nonlinear least squares: HRVi(t)=Mi+Ai•cos[2π•(t−θi)/T]+εi(i=1,…,388), where M i =average of HRV of the i th subject, A i =amplitude of HRV of the i th subject around M i , t=time-specific segment order number (one unit of t=30-minute, with t=1 indicating 7:00PM to7:30PM, 2 indicating 7:30PM to 8:00PM, etc. ), T=48 segments, θ i =lag from the reference time point (7:00PM) to the time of the zenith of the cosine curve fit to the data of the i th subject, and ε i =error term of the i th subject.…”

The circadian pattern of cardiac autonomic modulation in a middle-aged population

“…In the first stage, we fit one cosine periodic regression model for each individual to get the individual-level parameters ( M , A , and θ ) of the cosine curves as the indicators of nonlinear fluctuation of HRV. Specifically, in the first stage, we fit each HRV variable based on all available 5-min segments within 24 h from each participant to a cosine periodic regression model [23] using nonlinear least squares: , i  = 1, …, 95, in which M i is the daily average of HRV of the i th subject, A i is the fluctuation amplitude of HRV of the i th subject around M i , t is the time-specific segment order number, T is the total number of 5-min segments in 24 h, θ i is the acrophase (the lag from the reference time point (9 AM) to the time of the zenith of the cosine curve fit to the data of the i th subject), and ε i is the error term of the i th subject. One unit of t corresponds to 5 min, with 1 indicating 9:00 AM to 9:05 AM, 2 indicating 9:05 AM to 9:10 AM… etc.…”